COVID-19 stats¶

Find out¶

1. Wich country has maximum total cases, deaths, recovered and active cases?¶

2. What is the trend of confirmed deaths recovered active cases?¶

3. Visualize population to test done ratio¶

4. Show 20 countrys that are badly affected by COVID-19¶

5. Worst 20 countries having max confirmed cases.¶

6. Worst 20 countries having max total deaths.¶

7. Worst 20 countries having max active cases.¶

8. Worst 20 countries having max recovered cases.¶

9. Create a pie chart with wors affected countries¶

10. Deaths to confirmed ratio¶

11. Deaths to recovered ratio¶

12. Test to confirmed ratio¶

13. Serious to deaths ratio¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os

Data Extraction¶

In [2]:
####replace with your path
files = os.listdir(r'C:\Users\lucas\general\cursos\09-python\01_data_analyst_python\04-project_covid_19_stats\Covid-19')
files
Out[2]:
['country_wise_latest.csv',
 'covid_19_clean_complete.csv',
 'day_wise.csv',
 'full_grouped.csv',
 'usa_country_wise.csv',
 'worldometer_data.csv']
In [3]:
def read_data(path, filename):
    return pd.read_csv(path+'/'+filename)
In [4]:
path="C:/Users/lucas/general/cursos/09-python/01_data_analyst_python/04-project_covid_19_stats/Covid-19"
world_data = read_data(path, 'worldometer_data.csv')
world_data.head(3)
Out[4]:
Country/Region Continent Population TotalCases NewCases TotalDeaths NewDeaths TotalRecovered NewRecovered ActiveCases Serious,Critical Tot Cases/1M pop Deaths/1M pop TotalTests Tests/1M pop WHO Region
0 USA North America 3.311981e+08 5032179 NaN 162804.0 NaN 2576668.0 NaN 2292707.0 18296.0 15194.0 492.0 63139605.0 190640.0 Americas
1 Brazil South America 2.127107e+08 2917562 NaN 98644.0 NaN 2047660.0 NaN 771258.0 8318.0 13716.0 464.0 13206188.0 62085.0 Americas
2 India Asia 1.381345e+09 2025409 NaN 41638.0 NaN 1377384.0 NaN 606387.0 8944.0 1466.0 30.0 22149351.0 16035.0 South-EastAsia
In [5]:
day_wise = read_data(path, files[2])
In [11]:
group_data = read_data(path, files[3])
In [6]:
usa_data = read_data(path, files[4])
In [7]:
province_data = read_data(path, files[1])
In [14]:
province_data.shape
Out[14]:
(49068, 10)

Data transformation¶

In [48]:
day_wise.duplicated().sum()
group_data.duplicated().sum()
usa_data.duplicated().sum()
province_data.duplicated().sum()
Out[48]:
0
In [49]:
day_wise.isnull().sum()
group_data.isnull().sum()
usa_data.isnull().sum()
province_data.isnull().sum()
Out[49]:
Province/State    34404
Country/Region        0
Lat                   0
Long                  0
Date                  0
Confirmed             0
Deaths                0
Recovered             0
Active                0
WHO Region            0
dtype: int64

Data visualization¶

1. Wich country has maximum total cases, deaths, recovered and active cases?¶

In [50]:
world_data.columns
Out[50]:
Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')
In [9]:
import plotly.express as px
In [10]:
columns=['TotalCases', 'TotalDeaths', 'TotalRecovered', 'ActiveCases']
for i in columns:
    fig = px.treemap(world_data.iloc[0:20], values=i, path=['Country/Region'], 
               title='Treemap of different countries w.r.t to their {}'.format(i))
    fig.show()

2. What is the trend of confirmed deaths recovered active cases?¶

In [58]:
day_wise.head(3)
Out[58]:
Date Confirmed Deaths Recovered Active New cases New deaths New recovered Deaths / 100 Cases Recovered / 100 Cases Deaths / 100 Recovered No. of countries
0 2020-01-22 555 17 28 510 0 0 0 3.06 5.05 60.71 6
1 2020-01-23 654 18 30 606 99 1 2 2.75 4.59 60.00 8
2 2020-01-24 941 26 36 879 287 8 6 2.76 3.83 72.22 9
In [59]:
day_wise.columns
Out[59]:
Index(['Date', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'New cases',
       'New deaths', 'New recovered', 'Deaths / 100 Cases',
       'Recovered / 100 Cases', 'Deaths / 100 Recovered', 'No. of countries'],
      dtype='object')
In [11]:
px.line(day_wise, x='Date', y=['Confirmed', 'Deaths', 'Recovered', 'Active'], title='COVIDcases w.r. t to date', template='plotly_dark')

3. Visualize population to test done ratio¶

In [71]:
world_data.head(3)
Out[71]:
Country/Region Continent Population TotalCases NewCases TotalDeaths NewDeaths TotalRecovered NewRecovered ActiveCases Serious,Critical Tot Cases/1M pop Deaths/1M pop TotalTests Tests/1M pop WHO Region
0 USA North America 3.311981e+08 5032179 NaN 162804.0 NaN 2576668.0 NaN 2292707.0 18296.0 15194.0 492.0 63139605.0 190640.0 Americas
1 Brazil South America 2.127107e+08 2917562 NaN 98644.0 NaN 2047660.0 NaN 771258.0 8318.0 13716.0 464.0 13206188.0 62085.0 Americas
2 India Asia 1.381345e+09 2025409 NaN 41638.0 NaN 1377384.0 NaN 606387.0 8944.0 1466.0 30.0 22149351.0 16035.0 South-EastAsia
In [13]:
pop_test_ratio = world_data['Population']/world_data['TotalTests'].iloc[0:20]
In [14]:
fig=px.bar(world_data.iloc[0:20], x='Country/Region', y=pop_test_ratio[0:20], color='Country/Region', title='Pop to tests ratio')
fig.show()
In [ ]:

4. Show 20 countrys that are badly affected by COVID-19¶

In [82]:
world_data.columns
Out[82]:
Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')
In [15]:
px.bar(world_data.iloc[0:20], x='Country/Region', y=['Serious,Critical', 'TotalDeaths', 'TotalRecovered', 'ActiveCases', 'TotalCases'])

5. Worst 20 countries having max confirmed cases.¶

In [16]:
fig = px.bar(world_data.iloc[0:20], 
       x='TotalCases', 
       y='Country/Region',
          text='TotalCases',
        color='TotalCases'
      )
fig.update_layout(template='plotly_dark', title='20 countries having max confirmed cases')
fig.show()

6. Worst 20 countries having max total deaths.¶

In [ ]:
In [23]:
fig = px.bar(world_data.sort_values(by='TotalDeaths', ascending=False)[0:20], 
       x='TotalDeaths', 
       y='Country/Region',
          text='TotalDeaths',
        color='TotalDeaths'
      )
fig.update_layout(template='plotly_dark', title='20 countries having max TotalDeaths')
fig.show()

7. Worst 20 countries having max active cases.¶

In [24]:
fig = px.bar(world_data.sort_values(by='ActiveCases', ascending=False)[0:20], 
       x='ActiveCases', 
       y='Country/Region',
          text='ActiveCases',
        color='ActiveCases'
      )
fig.update_layout(template='plotly_dark', title='20 countries having max Active Cases')
fig.show()

8. Worst 20 countries recovered cases.¶

In [25]:
fig = px.bar(world_data.sort_values(by='TotalRecovered', ascending=False)[0:20], 
       x='TotalRecovered', 
       y='Country/Region',
          text='TotalRecovered',
        color='TotalRecovered'
      )
fig.update_layout(template='plotly_dark', title='20 countries having max Total Recovered Cases')
fig.show()

9. Create a pie chart with worst affected countries¶

In [28]:
labels=world_data[0:15]['Country/Region'].values
cases=['TotalCases', 'TotalDeaths', 'TotalRecovered', 'ActiveCases']
In [31]:
for i in cases:
    fig = px.pie(world_data[0:15],
           values=i,
           names=labels,
           title="{} recorded w.r.t to who region of 15 worst affected countries".format(i),
           hole=0.2,
           template="seaborn")
    fig.update_traces(textposition="inside", textinfo="value+percent+label")
    fig.show()

10. Deaths to confirmed ratio¶

In [33]:
deaths_to_confirmed = world_data['TotalDeaths']/world_data['TotalCases']
In [34]:
deaths_to_confirmed
Out[34]:
0      0.032353
1      0.033810
2      0.020558
3      0.016752
4      0.017845
         ...   
204    0.076923
205         NaN
206         NaN
207         NaN
208    0.100000
Length: 209, dtype: float64
In [45]:
fig = px.bar(world_data, 
       x='Country/Region', 
       y=deaths_to_confirmed
      )
fig.update_layout(template='plotly_dark', title='Deaths to confirmed ratio of worst affected countries')
fig.show()

11. Deaths to recovered ratio¶

In [40]:
deaths_to_recovered = world_data['TotalDeaths']/world_data['TotalRecovered']
In [46]:
fig = px.bar(world_data, 
       x='Country/Region', 
       y=deaths_to_recovered 
      )
fig.update_layout(template='plotly_dark', title='Deaths to recovered ratio of worst affected countries')
fig.show()

12. Test to confirmed ratio¶

In [42]:
deaths_to_test = world_data['TotalDeaths']/world_data['TotalTests']
In [47]:
fig = px.bar(world_data, 
       x='Country/Region', 
       y=deaths_to_test
      )
fig.update_layout(template='plotly_dark', title='Deaths to tests ratio of worst affected countries')
fig.show()

13. Serious to deaths ratio¶

In [44]:
serious_to_deaths = world_data['Serious,Critical']/world_data['TotalDeaths']
In [48]:
fig = px.bar(world_data, 
       x='Country/Region', 
       y=serious_to_deaths
      )
fig.update_layout(template='plotly_dark', title='Deaths to serious ratio of worst affected countries')
fig.show()
In [ ]: